I'll show you how to **build your own image from scratch** to run a single Uvicorn process. It is a simple process and is probably what you would want to do when using a distributed container management system like **Kubernetes**.

## Recap { #recap }

You can use multiple worker processes with the `--workers` CLI option with the `fastapi` or `uvicorn` commands to take advantage of **multi-core CPUs**, to run **multiple processes in parallel**.

    ```bash
    docker run -it --rm -v $PWD:/tmp -w /tmp tensorflow/build:2.15-python3.10
    ```

    Once you have the packages installed, you can run a specific unit test in
    bazel by doing as follows:

    ```bash
    export flags="--config=linux -k"
    ```

    If the tests are to be run on the GPU:

			}

			AssertObjEqual(t, toy, expect.Toys[idx], "ID", "CreatedAt", "UpdatedAt", "Name", "OwnerID", "OwnerType")
		}
	})

	t.Run("Company", func(t *testing.T) {
		AssertObjEqual(t, user.Company, expect.Company, "ID", "Name")
	})

	t.Run("Manager", func(t *testing.T) {
		if user.Manager != nil {
			if user.ManagerID == nil {
				t.Errorf("Manager's foreign key should be saved")
			} else {

   The root credentials can now be changed easily.

> Does this mean I need an enterprise KMS setup to run MinIO (securely)?

No, MinIO does not depend on any third-party KMS provider. You have three options here:

- Run MinIO without a KMS. In this case all IAM data will be stored in plain-text.
- Run MinIO with a single secret key. MinIO supports a static cryptographic key

) {
    QUICK_FEEDBACK_LINUX_ONLY(
        "Quick Feedback - Linux Only",
        "Run checks and functional tests (embedded executer, Linux)",
        "QuickFeedbackLinuxOnly",
    ),
    QUICK_FEEDBACK("Quick Feedback", "Run checks and functional tests (embedded executer, Windows)", "QuickFeedback"),
    PULL_REQUEST_FEEDBACK("Pull Request Feedback", "Run various functional tests", "PullRequestFeedback"),
    READY_FOR_NIGHTLY(

public final class PostStreamingWithPipe {
  public static final MediaType MEDIA_TYPE_MARKDOWN
      = MediaType.get("text/x-markdown; charset=utf-8");

  private final OkHttpClient client = new OkHttpClient();

  public void run() throws Exception {
    final PipeBody pipeBody = new PipeBody();

    Request request = new Request.Builder()
        .url("https://api.github.com/markdown/raw")
        .post(pipeBody)
        .build();

    assertTrue("Should run callable immediately", taskDone);
    assertEquals(6, (int) future.get());
  }

  public void testSameThreadScheduledExecutorWithException() throws InterruptedException {
    Runnable runnable =
        new Runnable() {
          @Override
          public void run() {
            throw new RuntimeException("Oh no!");
          }
        };

    assertTrue("Should run callable immediately", taskDone);
    assertEquals(6, (int) future.get());
  }

  public void testSameThreadScheduledExecutorWithException() throws InterruptedException {
    Runnable runnable =
        new Runnable() {
          @Override
          public void run() {
            throw new RuntimeException("Oh no!");
          }
        };

# "-devYYYYMMDD" to the end of the version string for nightly builds.
if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then
  export TF_VER_FULL="$(tfrun bazel run //tensorflow/tools/ci_build:calculate_full_version -- --wheel-type nightly)"
else
  export TF_VER_FULL="$(tfrun bazel run //tensorflow/tools/ci_build:calculate_full_version -- --wheel-type release)"
fi

# Note on gsutil commands:

# Execute um Servidor Manualmente

## Utilize o comando `fastapi run`

Em resumo, utilize o comando `fastapi run` para inicializar sua aplicação FastAPI:

<div class="termy">

```console
$ <font color="#4E9A06">fastapi</font> run <u style="text-decoration-style:solid">main.py</u>

  <span style="background-color:#009485"><font color="#D3D7CF"> FastAPI </font></span>  Starting production server 🚀